float32/caption.py

import argparse
import os
import ruamel_yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path

import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist


from models.epalm import ePALM
from models.utils import freeze_whole_model, unfreeze_parameters, print_trainable_params_percentage

 
from models.utils import filter_state, filter_msg, exclude_list

from transformers import AutoTokenizer


import utils



from dataset.caption import get_loader 

from scheduler import create_scheduler
from optim import create_optimizer
 


def train(model, data_loader, optimizer, tokenizer, epoch, warmup_steps, device, scheduler, config):
    model.train()  
    
    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))

    header = 'Train Epoch: [{}]'.format(epoch)
    print_freq = 50    
    step_size = 100
    warmup_iterations = warmup_steps*step_size  
    lm_loss_weight = config.get('lm_loss_weight', 1)
    append_eos_token = config.get('append_eos_token', False)
    eos_token = tokenizer.eos_token

    config_optim = utils.AttrDict(config['optimizer'])
    prompt_lr = config_optim.prompt_lr if hasattr(config_optim, 'prompt_lr') else None

    task_prompt = config.get('task_prompt', None)

    if prompt_lr is not None:
        metric_logger.add_meter('prompt_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))


    for i, batch in enumerate(metric_logger.log_every(data_loader, print_freq, header)):

        image = batch["images"].to(device,non_blocking=True)

        text = batch["sent"]

        if append_eos_token:
            text = [t.replace(eos_token, '') + eos_token for t in text]

        if task_prompt is not None:
            text = [task_prompt + ' ' + t for t in text]

        text_input = tokenizer(text, padding='longest', return_tensors="pt").to(device) 


        targets = text_input.input_ids.masked_fill(text_input.input_ids == tokenizer.pad_token_id, -100)
        

        answer_output = model(image=image, 
                              text=text_input, 
                              labels = targets,
                              return_dict = True,   
                              mode='train',
                              reduction='none',
                             )      
        
        loss = answer_output.loss         
        loss = loss.sum()/image.size(0)
        loss = loss*lm_loss_weight
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()    
        
        metric_logger.update(loss=loss.item())
        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
        if prompt_lr is not None:
            metric_logger.update(prompt_lr=optimizer.param_groups[1]["lr"])
        if epoch==0 and i%step_size==0 and i<=warmup_iterations: 
            scheduler.step(i//step_size) 

            
    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger.global_avg())     
    return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()} 




@torch.no_grad()
def evaluation(model, data_loader, tokenizer, device, config) :
    # test
    model.eval()
            
    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Generate Caption test result:'
    print_freq = 50
    
    predictions = []
    targets = []

    task_prompt = config.get('task_prompt', None)

    pad_token = tokenizer.pad_token
    eos_token = tokenizer.eos_token

    for n, batch in enumerate(metric_logger.log_every(data_loader, print_freq, header)):        
        
        image = batch["images"].to(device,non_blocking=True)
        text = ['' for q in image]  
        if task_prompt is not None:
            text = [task_prompt + ' ' + t for t in text]
        text_input = tokenizer(text, padding='longest', return_tensors="pt").to(device) 

        out = model(image=image, text=text_input, mode='generate', return_dict=True, max_length=30, do_sample=True)
        out_decode = []
        for i, o in enumerate(out):
            try:
                res = tokenizer.decode(o)
                response = res.split('</s>')[1].replace(pad_token, '').replace('</s>', '').replace(eos_token, '') # skip_special_tokens=True
            except TypeError:
                print(o)
                response = ' '
            out_decode.append(response)

        predictions.extend(out_decode)

        if 'targets' in batch:
            targets.extend(batch['targets'])


    evaluator = data_loader.evaluator
    eval_results = evaluator.evaluate(predictions, targets)

    wandb_log_dict = {}

    for score_name, score in eval_results.items():
        wandb_log_dict[f'Valid/{score_name}'] = score


    print(wandb_log_dict)

    return wandb_log_dict



def main(args, config):
    os.environ['TORCH_HOME'] = os.environ['XDG_CACHE_HOME']+'/torch'
    utils.init_distributed_mode(args)    
    
    device = torch.device(args.device)

    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    cudnn.benchmark = True
    
    start_epoch = 0
    max_epoch = config['schedular']['epochs']
    warmup_steps = config['schedular']['warmup_epochs']
    
    print(args, config)


    tokenizer = AutoTokenizer.from_pretrained(args.text_model, use_fast=False, local_files_only=True)


    if args.distributed:
        num_tasks = utils.get_world_size()
        global_rank = utils.get_rank()     
    else:
        num_tasks = None
        global_rank = None


    #########
    num_workers = config.get('num_workers', 4)
    train_topk = config.get('train_topk', -1)
    valid_topk = config.get('valid_topk', -1)
    data_dir = args.data_dir

    args.image_size = config.get('image_res', 224)
    args.use_data_augmentation = True 


    train_loader = get_loader(
        args,
        split='train', mode='train', batch_size=config['batch_size_train'],
        distributed=args.distributed,
        workers=num_workers,
        topk=train_topk,
        data_dir=data_dir,
        local_rank=global_rank, world_size=num_tasks, verbose=True
    )

    print('# len train loader:', len(train_loader))
    print(f'Building val loader')
    val_loader = get_loader(
        args,
        split='val', mode='val', batch_size=config['batch_size_test'],
        distributed=False, 
        workers=4,
        topk=valid_topk,data_dir=data_dir,
        local_rank=global_rank, world_size=num_tasks, verbose=True
    )
    print('# len val loader:', len(val_loader))

    print(f'Building test loader')
    test_loader = get_loader(
        args,
        split='test', mode='val', batch_size=config['batch_size_test'],
        distributed=False, 
        workers=4,
        topk=valid_topk,data_dir=data_dir,
        local_rank=global_rank, world_size=num_tasks, verbose=True
    )


    print('# len test loader:', len(test_loader))

    #### Model #### 
    print("Creating model")
    
    start_layer_idx = config.get('start_layer_idx', 0)
    end_layer_idx = config.get('end_layer_idx', 0)

    vision_model_name = config.get('vision_model_name', args.vision_model)

    model = ePALM(opt_model_name = args.text_model, 
                   vision_model_name = vision_model_name, 
                   use_vis_prefix = True, 
                   start_layer_idx = start_layer_idx, 
                   end_layer_idx = end_layer_idx, 
                   return_hidden_state_vision = True, 
                   config=config,
    )
    
        
    model = model.to(device)   
    
    arg_opt = utils.AttrDict(config['optimizer'])
    optimizer = create_optimizer(arg_opt, model, config=config)

    if hasattr(arg_opt, 'prompt_lr') and arg_opt.prompt_lr is not None:
        print('\tInitial other params params lr: %f' % optimizer.param_groups[0]['lr'])
        print('\tInitial prompt params lr: %f' % optimizer.param_groups[1]['lr'])

    arg_sche = utils.AttrDict(config['schedular'])
    lr_scheduler, _ = create_scheduler(arg_sche, optimizer)          
         
    best_epoch = 0 
    best_valid = 0 
    
    if args.checkpoint:    

        checkpoint = torch.load(args.checkpoint, map_location='cpu') 
        state_dict = checkpoint['model']
        msg = model.load_state_dict(state_dict,strict=False)  
        msg = filter_msg(msg, exclude_list)
        print('load checkpoint from %s'%args.checkpoint)
        print(msg)  

        if args.resume:
            model = model.to(device) 
            optimizer.load_state_dict(checkpoint['optimizer'])
            lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
            start_epoch = checkpoint['epoch']+1  
            print(checkpoint.keys())
            if 'best_valid' in checkpoint:
                best_valid = checkpoint['best_valid'] 
                best_epoch = checkpoint['best_epoch'] 
                print("load best valid {} at epoch {}".format(best_valid, best_epoch))

    
    freeze_whole_model(model)
    unfreeze_parameters(model, config)
    print_trainable_params_percentage(model)
    
    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        model_without_ddp = model.module    
    
    
    print("Start training")
    start_time = time.time()


    for epoch in range(start_epoch, max_epoch):
        if epoch>0:
            lr_scheduler.step(epoch+warmup_steps)  
        
        if not args.evaluate:
            if args.distributed:
                train_loader.sampler.set_epoch(epoch)

            train_stats = train(model, train_loader, optimizer, tokenizer, epoch, warmup_steps, device, lr_scheduler, config)  

        if args.evaluate:
            break
            

        valid_results = evaluation(model, val_loader, tokenizer, device, config) 

        if utils.is_main_process():               
            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                         'epoch': epoch,
                        }                
            with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
                f.write(json.dumps(log_stats) + "\n")                        
                    
            save_obj = {
                'model': filter_state(model_without_ddp.state_dict(), exclude_list),
                'optimizer': optimizer.state_dict(),
                'lr_scheduler': lr_scheduler.state_dict(),
                'config': config,
                'epoch': epoch,
                'best_valid': best_valid,
                'best_epoch': best_epoch,
            }

            if args.save_best:
                valid_score = valid_results['Valid/CIDEr']

                if valid_score > best_valid or epoch == 0:
                    best_valid = valid_score
                    best_epoch = epoch
                    print("Save best epoch:", best_epoch)

                    save_obj['best_valid'] = best_valid
                    save_obj['best_epoch'] = best_epoch

                    torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth'))  
            torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_last.pth'))  


        dist.barrier()   
    




    ### test best model
    if not args.evaluate:
        checkpoint = torch.load(os.path.join(args.output_dir, 'checkpoint_best.pth'), map_location='cpu') 
        state_dict = checkpoint['model']   
        msg = model.module.load_state_dict(state_dict,strict=False)  
        msg = filter_msg(msg, exclude_list)
        print('load checkpoint for test from %s'%os.path.join(args.output_dir, 'checkpoint_best.pth'))
        print(msg)
    vqa_result = evaluation(model, test_loader, tokenizer, device, config)    

                     
    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Training time {}'.format(total_time_str)) 
    
            

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--config', default='./configs/VQA.yaml') 
    parser.add_argument('--checkpoint', default='') 
    parser.add_argument('--output_dir', default='output/vqa')
    parser.add_argument('--evaluate', action='store_true')    
    parser.add_argument('--text_model', default='facebook/opt-350m')
    parser.add_argument('--vision_model', default='vit_base_patch16_224')
    parser.add_argument('--device', default='cuda')
    parser.add_argument('--seed', default=42, type=int)
    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
    parser.add_argument('--distributed', default=True, type=bool)
    
    parser.add_argument('--data_dir', default='/data/mshukor/data')   
    parser.add_argument('--resume', action='store_true')    

    parser.add_argument('--save_best', action='store_true') 
    
    parser.add_argument('--image_dir', default='/data/mshukor/data')   

    

    args = parser.parse_args()

    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)

    args.result_dir = os.path.join(args.output_dir, 'result')

    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    Path(args.result_dir).mkdir(parents=True, exist_ok=True)
        
    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
    
    main(args, config)